Format

Send to

Choose Destination
Genome Biol. 2015 Jan 24;16:14. doi: 10.1186/s13059-015-0581-9.

Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements.

Author information

1
Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA. wz31@duke.edu.
2
Department of Twin Research and Genetic Epidemiology, King's College London, London, UK. tim.spector@kcl.ac.uk.
3
William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK. p.deloukas@qmul.ac.uk.
4
Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, 21589, Saudi Arabia. p.deloukas@qmul.ac.uk.
5
Department of Twin Research and Genetic Epidemiology, King's College London, London, UK. jordana.bell@kcl.ac.uk.
6
Department of Computer Science, Princeton University, Princeton, NJ, USA. bee@princeton.edu.

Abstract

BACKGROUND:

Recent assays for individual-specific genome-wide DNA methylation profiles have enabled epigenome-wide association studies to identify specific CpG sites associated with a phenotype. Computational prediction of CpG site-specific methylation levels is critical to enable genome-wide analyses, but current approaches tackle average methylation within a locus and are often limited to specific genomic regions.

RESULTS:

We characterize genome-wide DNA methylation patterns, and show that correlation among CpG sites decays rapidly, making predictions solely based on neighboring sites challenging. We built a random forest classifier to predict methylation levels at CpG site resolution using features including neighboring CpG site methylation levels and genomic distance, co-localization with coding regions, CpG islands (CGIs), and regulatory elements from the ENCODE project. Our approach achieves 92% prediction accuracy of genome-wide methylation levels at single-CpG-site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs and is robust across platform and cell-type heterogeneity. Our classifier outperforms other types of classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation, CGIs, co-localized DNase I hypersensitive sites, transcription factor binding sites, and histone modifications were found to be most predictive of methylation levels.

CONCLUSIONS:

Our observations of DNA methylation patterns led us to develop a classifier to predict DNA methylation levels at CpG site resolution with high accuracy. Furthermore, our method identified genomic features that interact with DNA methylation, suggesting mechanisms involved in DNA methylation modification and regulation, and linking diverse epigenetic processes.

PMID:
25616342
PMCID:
PMC4389802
DOI:
10.1186/s13059-015-0581-9
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center